Non-coding RNAs (ncRNAs) play crucial roles in drug resistance and sensitivity, making them important biomarkers and therapeutic targets. However, predicting ncRNA-drug associations is challenging due to issues such as dataset imbalance and sparsity, limiting the identification of robust biomarkers. Existing models often fall short in capturing local and global sequence information, limiting the reliability of predictions. This study introduces DMGAT (diffusion map and heterogeneous graph attention network), a novel deep learning model designed to predict ncRNA-drug associations. DMGAT integrates diffusion maps for sequence embedding, graph convolutional networks for feature extraction, and GAT for heterogeneous information fusion. To address dataset imbalance, the model incorporates sensitivity associations and employs a random forest classifier to select reliable negative samples. DMGAT embeds ncRNA sequences and drug SMILES using the word2vec technique, capturing local and global sequence information. The model constructs a heterogeneous network by combining sequence similarity and Gaussian Interaction Profile kernel similarity, providing a comprehensive representation of ncRNA-drug interactions. Evaluated through five-fold cross-validation on a curated dataset from NoncoRNA and ncDR, DMGAT outperforms seven state-of-the-art methods, achieving the highest area under the receiver operating characteristic curve (0.8964), area under the precision-recall curve (0.8984), recall (0.9576), and F1-score (0.8285). The raw data are released to Zenodo with identifier 13929676. The source code of DMGAT is available at https://github.com/liutingyu0616/DMGAT/tree/main.

In recent years, learning-based methods have performed remarkably well in underwater image enhancement, but their performance is limited by the lack of high-quality, diverse training datasets. Current underwater image datasets are unable to address the following three issues: intra-domain gaps in underwater environments, inter-domain gaps between synthetic and real data, and domain inaccuracies. To overcome these limitations, we construct a realistic underwater scene using 3D graphics engine through a three-step approach: 1) integrate a simulation-specific underwater light propagation models to create volumetric fog; 2) employ physical model-based rendering for accurate light field simulation; 3) configure scenes with parameters extracted from real underwater images. Based on this framework, we develop an underwater image enhancement dataset (MUSE). Experiments demonstrate that models trained on MUSE outperform those trained on conventional datasets, highlighting the effectiveness of our approach.

Language models based on neural network primarily focus on learning patterns and representations from the input data, without explicitly modeling the grammatical structure of the text, thus often lacks of the consideration of syntax information. The distributional compositional categorical (DisCoCat) model is a framework that can both capture the distributional information of words and the syntactic structure of sentence. Thus, we research the application of DisCoCat models in both quantum and classical natural language processing. Specifically, in classical and quantum natural language processing, we respectively converted sentences into tensor networks and quantum circuits based on the DisCoCat model to get representation containing both semantic and syntactic information of language in classical and quantum computer. We conduct experiment for a text binary classification task in both classical and quantum natural language processing setting to verify the effectiveness of DisCoCat model. The experimental results shown that the combination of DisCoCat with quantum computing has significant potential.

Traditional recommendation models usually consider only one type of user behavior, some recommendation models focus on user behaviors that frequently occur (e.g. click, view), which have more interaction data but does not accurately reflect the users’ interests, and another types of recommendation models focus on user behaviors which reflect a stronger user preference (e.g. purchase), but has problem of data sparcity. Therefore, in order to improve the performance of the model, multi-behavior-based recommendation is a suitable solution. However, early efforts towards multi-behavior recommendation either do not utilize external knowledge to encode explicit relationships, or cannot fully explore the semantic information of user behavior. Hence, in this work, we solve these problems by constructing a heterogeneous interaction graph and proposing a new multi-behavior recommendation model named KMBFD(short for Knowledge-based Multi-Behavior Recommendation with Factor Disentanglement), which encodes knowledge-based information by multi-relational item graph and user graph and captures semantic information of multi-behavior interaction by a feature aggregation module with factor disentanglement method that is used to disentangle multi-dimensional latent factors hidden in the training data, which can enhance expressiveness and interpretability.

Integrating knowledge into text is a promising way to enrich text representation, especially in the medical field.However, undifferentiated knowledge not only confuses the text representation but also imports unexpected noises.In this paper, to alleviate this problem, we propose leveraging capsule routing to associate knowledge with medical literature hierarchically (called HiCapsRKL).Firstly, HiCapsRKL extracts two empirically designed text fragments from medical literature and encodes them into fragment representations respectively.Secondly, the capsule routing algorithm is applied to two fragment representations.Through the capsule computing and dynamic routing, each representation is processed into a new representation (denoted as caps-representation), and we integrate the caps-representations as information gain to associate knowledge with medical literature hierarchically.Finally, HiCapsRKL are validated on relevance prediction and medical literature retrieval test sets.The experimental results and analyses show that HiCapsRKL can more accurately associate knowledge with medical literature than the mainstream methods.In summary, HiCapsRKL can efficiently help selecting the most relevant knowledge to the medical literature, which may be an alternative attempt to improve knowledge-based text representation.Source code is released on GitHub 1 .